FIA NLS Models: Biomass Growth vs. Stand Age – Additional Growth Estimators
JAH
2023-04-25
Background
This analysis document compliments FIA - NLS Models: Biomass Growth vs. Stand Age. All of the background information from that document applies to these analyses, which are extensions to them. The difference between that document and this analysis is the use of different growth estimators.
Here, we fit the models using: 1) calculated plot biomass growth (Mass-Balance method) using only trees >5 inches (12.5 cm) dbh (\(G_{MassBal > 5}\)), and 2) plot biomass growth (tree incremental growth method) for trees >5 inches (12.5 cm) dbh (\(G_{TI-NoIngrow}\)).
Below the model fitting procedure is implemented by ecoprovince:
Analysis 1: \(G_{MassBal > 5}\)
211 - Northeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6869 3003.0
## 2 6817 2319.6 52 683.36 38.621 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 29089.03
## 2 2 27204.41##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.38487 0.27356 5.062 4.25e-07 ***
## alpha 0.18115 0.03979 4.553 5.39e-06 ***
## a 0.52635 0.16513 3.187 0.00144 **
## b 2.18400 0.19076 11.449 < 2e-16 ***
## c 52.12308 0.96256 54.151 < 2e-16 ***
## d 1.31622 0.08559 15.379 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5833 on 6817 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (54 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 19351 10991.7
## 2 18862 6848.8 489 4142.9 23.333 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 82798.28
## 2 2 72717.04##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.04368 0.24809 8.238 < 2e-16 ***
## alpha 0.18402 0.02816 6.535 6.53e-11 ***
## a 0.33754 0.02113 15.978 < 2e-16 ***
## b 1.67224 0.06022 27.767 < 2e-16 ***
## c 41.35770 0.42247 97.894 < 2e-16 ***
## d 1.23219 0.02206 55.859 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6026 on 18862 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (3847 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 45 rows containing missing values (`geom_point()`).
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7319 3321.1
## 2 7255 2929.5 64 391.6 15.153 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 33832.56
## 2 2 32731.56##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.60471 0.14205 -4.257 2.10e-05 ***
## alpha 0.54725 0.04161 13.152 < 2e-16 ***
## a 1.24080 0.45261 2.741 0.00613 **
## b 3.34106 0.46029 7.259 4.32e-13 ***
## c 46.44359 1.92195 24.165 < 2e-16 ***
## d 1.75442 0.20430 8.588 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6354 on 7255 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (72 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 6 rows containing missing values (`geom_point()`).
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5044 2546.6
## 2 4824 1164.1 220 1382.5 26.04 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 25129.16
## 2 2 20639.78##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.27611 0.26038 1.060 0.289
## alpha 0.40648 0.05148 7.896 3.54e-15 ***
## a 0.75525 0.11162 6.766 1.48e-11 ***
## b 2.61616 0.16019 16.332 < 2e-16 ***
## c 52.52465 1.61457 32.532 < 2e-16 ***
## d 1.34330 0.07403 18.147 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4912 on 4824 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1015 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 7 rows containing missing values (`geom_point()`).
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8872 3730.1
## 2 8730 2486.8 142 1243.2 30.736 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 40473.24
## 2 2 36461.41##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.46732 0.13024 -3.588 0.000335 ***
## alpha 0.37517 0.04337 8.651 < 2e-16 ***
## a 1.27585 0.34147 3.736 0.000188 ***
## b 2.57339 0.34328 7.497 7.19e-14 ***
## c 36.25752 1.19232 30.409 < 2e-16 ***
## d 1.48024 0.16001 9.251 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5337 on 8730 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1274 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: Removed 6 rows containing missing values (`geom_point()`).
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 13446 8033.9
## 2 13195 6851.0 251 1182.9 9.0765 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 69488.49
## 2 2 66713.48##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.25713 0.23997 9.406 < 2e-16 ***
## alpha 0.60282 0.02224 27.100 < 2e-16 ***
## a 0.66299 0.08902 7.448 1.01e-13 ***
## b 3.47069 0.14354 24.180 < 2e-16 ***
## c 24.60260 0.33688 73.030 < 2e-16 ***
## d 1.53924 0.04462 34.494 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7206 on 13195 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (316 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 30 rows containing missing values (`geom_point()`).
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 13504 9840.7
## 2 13221 9032.3 283 808.4 4.1812 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 69847.07
## 2 2 67892.53##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.82450 0.24314 7.504 6.58e-14 ***
## alpha 0.58974 0.02275 25.921 < 2e-16 ***
## a 0.52603 0.11031 4.769 1.87e-06 ***
## b 3.39759 0.16067 21.146 < 2e-16 ***
## c 23.73576 0.38312 61.954 < 2e-16 ***
## d 1.62726 0.05587 29.128 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8265 on 13221 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (402 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 66 rows containing missing values (`geom_point()`).
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1368 1225.98
## 2 1316 933.16 52 292.82 7.9413 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 7586.402
## 2 2 7029.295##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.64588 1.04477 1.575 0.1154
## alpha 0.71337 0.09113 7.828 1.02e-14 ***
## a 1.72798 1.57006 1.101 0.2713
## b 2.11173 1.58240 1.335 0.1823
## c 26.02966 3.65593 7.120 1.77e-12 ***
## d 1.54134 0.92973 1.658 0.0976 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8421 on 1316 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (66 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89837, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.0397, p-value = 4.663e-07
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 5 rows containing missing values (`geom_point()`).
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1888 947.45
## 2 1773 380.66 115 566.79 22.956 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 9343.582
## 2 2 7300.531##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.06042 0.55389 1.914 0.05572 .
## alpha 0.05979 0.10882 0.549 0.58278
## a 1.57196 0.35126 4.475 8.12e-06 ***
## b 0.95517 0.34402 2.776 0.00555 **
## c 40.21823 3.57484 11.250 < 2e-16 ***
## d 1.22899 0.37861 3.246 0.00119 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4634 on 1773 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (516 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.81074, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.9337, p-value = 4.099e-12
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 2 rows containing missing values (`geom_point()`).
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 710 1183.68
## 2 667 955.57 43 228.1 3.7028 2.639e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3587.653
## 2 2 3324.689##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.5528 1.8132 0.856 0.39210
## alpha 0.4145 0.1902 2.179 0.02971 *
## a 0.8692 0.3061 2.840 0.00466 **
## b 1.8594 0.6041 3.078 0.00217 **
## c 24.2189 2.2757 10.643 < 2e-16 ***
## d 0.8635 0.1783 4.844 1.58e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.197 on 667 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (44 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.91049, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.8413, p-value = 0.0001224
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 2 rows containing missing values (`geom_point()`).
plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
332 - Great Plains Steppe
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
341 - Intermountain Semi-desert & Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
411 - Everglades
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6765 2158.6
## 2 6741 2080.3 24 78.309 10.573 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 25640.9
## 2 2 25357.7##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.05246 0.33762 6.079 1.27e-09 ***
## alpha 0.19626 0.03521 5.574 2.58e-08 ***
## a 0.28168 0.10946 2.573 0.0101 *
## b 2.02187 0.14790 13.671 < 2e-16 ***
## c 58.28046 1.20100 48.527 < 2e-16 ***
## d 1.38453 0.07252 19.091 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5555 on 6741 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (25 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8308 4415.0
## 2 8253 4106.1 55 308.95 11.29 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 40211.66
## 2 2 39423.66##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.5859 0.2011 2.913 0.00359 **
## alpha 0.5816 0.0560 10.386 < 2e-16 ***
## a 1.3424 0.4624 2.903 0.00371 **
## b 2.7847 0.4599 6.055 1.47e-09 ***
## c 33.9072 1.3047 25.988 < 2e-16 ***
## d 1.5505 0.2059 7.529 5.65e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7054 on 8253 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (56 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 2 rows containing missing values (`geom_point()`).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 890 500.00
## 2 883 494.01 7 5.9895 1.5294 0.1536
## model AIC
## 1 1 3721.381
## 2 2 3697.880##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 2.4737 1.3321 1.857 0.063637 .
## alpha 0.3960 0.1599 2.476 0.013465 *
## a 1.5488 0.3126 4.954 8.71e-07 ***
## b 1.2686 0.3565 3.559 0.000393 ***
## c 29.6844 1.9960 14.872 < 2e-16 ***
## d 0.4998 0.1042 4.799 1.87e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.748 on 883 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (7 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.94889, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.1798, p-value = 0.001474
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1000 566.90
## 2 987 516.99 13 49.915 7.3303 7.303e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4308.841
## 2 2 4184.325##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 3.3797 1.7725 1.907 0.0569 .
## alpha 0.4663 0.1130 4.126 4.00e-05 ***
## a 0.0000 4.4807 0.000 1.0000
## b 1.8149 4.4748 0.406 0.6851
## c 26.3622 4.6564 5.662 1.97e-08 ***
## d 2.6767 3.9677 0.675 0.5001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7237 on 987 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (13 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.94511, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.1117, p-value = 9.858e-10
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M242 - Cascade Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3140 2796.3
## 2 3127 2664.1 13 132.19 11.935 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 17424.89
## 2 2 17231.79##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.62499 0.29743 -5.463 5.04e-08 ***
## alpha 0.94738 0.07600 12.466 < 2e-16 ***
## a 6.32427 0.60136 10.517 < 2e-16 ***
## b 5.07175 0.95536 5.309 1.18e-07 ***
## c 34.84127 1.54541 22.545 < 2e-16 ***
## d 0.31391 0.05278 5.948 3.02e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.923 on 3127 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (91 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92896, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -14.134, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 14 rows containing missing values (`geom_point()`).
plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1681 625.59
## 2 1668 611.40 13 14.183 2.9764 0.0002512 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 8778.340
## 2 2 8695.476##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -2.32950 0.25968 -8.971 < 2e-16 ***
## alpha 0.55587 0.11127 4.996 6.48e-07 ***
## a 6.75834 0.67411 10.026 < 2e-16 ***
## b 7.53989 1.40145 5.380 8.50e-08 ***
## c 31.57505 0.96997 32.553 < 2e-16 ***
## d 0.19588 0.03937 4.975 7.21e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6054 on 1668 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (303 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89657, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.8998, p-value = 9.628e-05
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 9 rows containing missing values (`geom_point()`).
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 360 160.54
## 2 359 159.48 1 1.0664 2.4005 0.1222
## model AIC
## 1 1 1023.403
## 2 2 1022.970##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -2.3791 0.3247 -7.326 1.58e-12 ***
## alpha 0.2538 0.1576 1.610 0.108204
## a 0.0000 5.2542 0.000 1.000000
## b 3.2741 5.3043 0.617 0.537455
## c 58.2488 17.1354 3.399 0.000751 ***
## d 2.0200 2.3362 0.865 0.387822
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6665 on 359 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (2 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.94694, p-value = 3.647e-10
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = 0.17226, p-value = 0.8632
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1736 1481.0
## 2 1719 1349.9 17 131.13 9.8227 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 5173.137
## 2 2 5002.900##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.68338 0.62335 -1.096 0.273
## alpha 0.48981 0.06448 7.597 4.96e-14 ***
## a 0.72851 0.17907 4.068 4.95e-05 ***
## b 1.17595 0.22440 5.240 1.80e-07 ***
## c 63.66139 4.20757 15.130 < 2e-16 ***
## d 1.13138 0.16134 7.012 3.36e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8862 on 1719 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (31 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.85337, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.6803, p-value = 2.864e-06
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 7 rows containing missing values (`geom_point()`).
plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2527 1959.3
## 2 2485 1819.5 42 139.81 4.5466 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 9211.166
## 2 2 9025.020##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.5737 0.5748 -0.998 0.318
## alpha 0.6926 0.0598 11.582 < 2e-16 ***
## a 0.5800 0.1237 4.688 2.90e-06 ***
## b 1.8716 0.2791 6.706 2.47e-11 ***
## c 79.9119 4.7168 16.942 < 2e-16 ***
## d 1.4716 0.1221 12.048 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8557 on 2485 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (121 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.87891, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.7054, p-value = 1.16e-08
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 28 rows containing missing values (`geom_point()`).
plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1699 965.89
## 2 1670 879.44 29 86.456 5.6612 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 7044.476
## 2 2 6860.189##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.19761 0.80332 -0.246 0.806
## alpha 0.75046 0.06295 11.921 < 2e-16 ***
## a 0.81952 0.19093 4.292 1.87e-05 ***
## b 3.07569 0.58099 5.294 1.36e-07 ***
## c 52.55816 1.85863 28.278 < 2e-16 ***
## d 1.27999 0.08125 15.754 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7257 on 1670 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (77 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.92247, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.6369, p-value = 3.537e-06
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 14 rows containing missing values (`geom_point()`).
plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Error in nls(fg1_MBg5, data = G_M334, start = c(tau = tau.start, a = a.start, :
## Convergence failure: singular convergence (7)
## model AIC
## 1 1 NA
## 2 2 1309.945##
## Formula: G_MassBal_g5_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) *
## (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -3.4115 0.1414 -24.130 < 2e-16 ***
## alpha 0.7070 0.1736 4.073 5.74e-05 ***
## a 0.0000 46.3162 0.000 1.00000
## b 3.8782 46.1586 0.084 0.93309
## c 73.7708 27.1585 2.716 0.00693 **
## d 2.1008 14.6687 0.143 0.88620
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4355 on 349 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (104 observations deleted due to missingness)summary
- simple model: does not fit
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89926, p-value = 1.373e-14
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.5551, p-value = 0.0003779
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (`geom_point()`).
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 2 |
| 212 | Laurentian Mixed Forest | 2 |
| 221 | Eastern Broadleaf Forest | 2 |
| 222 | Midwest Broadleaf Forest | 2 |
| 223 | Central Interior Broadleaf Forest | 2 |
| 231 | Southeastern Mixed Forest | 2 |
| 232 | Outer Coastal Plain Mixed Forest | 2 |
| 234 | Lower Mississippi Riverine Forest | 2 |
| 242 | Pacific Lowland Mixed Forest | NA |
| 251 | Prairie Parkland (Temperate) | 2 |
| 255 | Prairie Parkland (Subtropical) | 2 |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | NA |
| 332 | Great Plains Steppe | NA |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | NA |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 2 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 2 |
| M223 | Ozark Broadleaf Forest Meadow | 2 |
| M231 | Ouachita Mixed Forest | 2 |
| M242 | Cascade Mixed Forest | 2 |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 2 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | 2 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | 2 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | 2 |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | 2 |
| M334 | Black Hills Coniferous Forest | 2 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | tau | tau.variance | tau.2.5 | tau.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | a | a.2.5 | a.97.5 | b | b.2.5 | b.97.5 | c | c.2.5 | c.97.5 | d | d.2.5 | d.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 6877 | 2876 | 1.3848730 | 0.0748353 | 0.8486091 | 1.9211370 | 0.1811536 | 0.0015833 | 0.1031516 | 0.2591555 | 0.5263456 | 0.2026339 | 0.8500573 | 2.1840036 | 1.8100492 | 2.557958 | 52.12308 | 50.23617 | 54.00999 | 1.3162156 | 1.1484390 | 1.4839923 |
| 212 | Laurentian Mixed Forest | east | 22715 | 9499 | 2.0436775 | 0.0615505 | 1.5573915 | 2.5299635 | 0.1840190 | 0.0007930 | 0.1288230 | 0.2392149 | 0.3375402 | 0.2961316 | 0.3789487 | 1.6722398 | 1.5541956 | 1.790284 | 41.35770 | 40.52962 | 42.18579 | 1.2321897 | 1.1889520 | 1.2754274 |
| 221 | Eastern Broadleaf Forest | east | 7333 | 3571 | -0.6047095 | 0.0201792 | -0.8831755 | -0.3262435 | 0.5472479 | 0.0017315 | 0.4656788 | 0.6288170 | 1.2408049 | 0.3535657 | 2.1280442 | 3.3410563 | 2.4387504 | 4.243362 | 46.44359 | 42.67601 | 50.21117 | 1.7544197 | 1.3539400 | 2.1548993 |
| 222 | Midwest Broadleaf Forest | east | 5845 | 2589 | 0.2761072 | 0.0677959 | -0.2343492 | 0.7865636 | 0.4064780 | 0.0026500 | 0.3055568 | 0.5073992 | 0.7552514 | 0.5364225 | 0.9740803 | 2.6161630 | 2.3021221 | 2.930204 | 52.52465 | 49.35936 | 55.68994 | 1.3433048 | 1.1981811 | 1.4884284 |
| 223 | Central Interior Broadleaf Forest | east | 10010 | 3864 | -0.4673159 | 0.0169623 | -0.7226155 | -0.2120163 | 0.3751687 | 0.0018806 | 0.2901611 | 0.4601764 | 1.2758492 | 0.6064927 | 1.9452056 | 2.5733896 | 1.9004829 | 3.246296 | 36.25752 | 33.92030 | 38.59474 | 1.4802421 | 1.1665894 | 1.7938948 |
| 231 | Southeastern Mixed Forest | east | 13517 | 6193 | 2.2571276 | 0.0575847 | 1.7867555 | 2.7274998 | 0.6028199 | 0.0004948 | 0.5592179 | 0.6464220 | 0.6629863 | 0.4884947 | 0.8374779 | 3.4706941 | 3.1893379 | 3.752050 | 24.60260 | 23.94226 | 25.26294 | 1.5392390 | 1.4517707 | 1.6267073 |
| 232 | Outer Coastal Plain Mixed Forest | east | 13629 | 6626 | 1.8244973 | 0.0591150 | 1.3479164 | 2.3010781 | 0.5897383 | 0.0005176 | 0.5451419 | 0.6343347 | 0.5260296 | 0.3098075 | 0.7422517 | 3.3975908 | 3.0826554 | 3.712526 | 23.73576 | 22.98479 | 24.48674 | 1.6272648 | 1.5177596 | 1.7367699 |
| 234 | Lower Mississippi Riverine Forest | east | 1388 | 778 | 1.6458810 | 1.0915390 | -0.4037106 | 3.6954726 | 0.7133667 | 0.0083055 | 0.5345821 | 0.8921514 | 1.7279771 | -1.3521265 | 4.8080806 | 2.1117320 | -0.9925619 | 5.216026 | 26.02966 | 18.85758 | 33.20174 | 1.5413404 | -0.2825841 | 3.3652649 |
| 242 | Pacific Lowland Mixed Forest | pacific | 83 | 83 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 251 | Prairie Parkland (Temperate) | east | 2295 | 906 | 1.0604150 | 0.3067958 | -0.0259339 | 2.1467640 | 0.0597909 | 0.0118427 | -0.1536466 | 0.2732284 | 1.5719618 | 0.8830379 | 2.2608857 | 0.9551704 | 0.2804365 | 1.629904 | 40.21823 | 33.20688 | 47.22958 | 1.2289884 | 0.4864271 | 1.9715497 |
| 255 | Prairie Parkland (Subtropical) | east | 717 | 319 | 1.5527641 | 3.2876685 | -2.0074891 | 5.1130173 | 0.4144618 | 0.0361922 | 0.0409157 | 0.7880080 | 0.8691588 | 0.2681406 | 1.4701769 | 1.8594003 | 0.6731608 | 3.045640 | 24.21891 | 19.75060 | 28.68721 | 0.8634886 | 0.5134683 | 1.2135090 |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 25 | 25 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 163 | 161 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | interior west | 218 | 218 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 4 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 9 | 9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 3 | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 331 | 255 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 332 | Great Plains Steppe | interior west | 232 | 128 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 341 | Intermountain Semi-Desert and Desert | interior west | 66 | 64 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 124 | 123 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 411 | Everglades | east | 96 | 63 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 6772 | 3006 | 2.0524650 | 0.1139898 | 1.3906157 | 2.7143142 | 0.1962572 | 0.0012396 | 0.1272394 | 0.2652749 | 0.2816799 | 0.0670973 | 0.4962624 | 2.0218714 | 1.7319418 | 2.311801 | 58.28046 | 55.92613 | 60.63480 | 1.3845270 | 1.2423570 | 1.5266971 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 8315 | 3810 | 0.5858719 | 0.0404452 | 0.1916460 | 0.9800977 | 0.5815931 | 0.0031358 | 0.4718230 | 0.6913633 | 1.3423766 | 0.4359066 | 2.2488467 | 2.7846809 | 1.8831181 | 3.686244 | 33.90718 | 31.34955 | 36.46481 | 1.5504926 | 1.1468078 | 1.9541773 |
| M223 | Ozark Broadleaf Forest Meadow | east | 896 | 349 | 2.4737178 | 1.7744035 | -0.1406699 | 5.0881054 | 0.3960003 | 0.0255750 | 0.0821289 | 0.7098717 | 1.5488194 | 0.9352237 | 2.1624150 | 1.2685974 | 0.5689455 | 1.968249 | 29.68435 | 25.76689 | 33.60181 | 0.4998401 | 0.2954160 | 0.7042642 |
| M231 | Ouachita Mixed Forest | east | 1006 | 495 | 3.3796722 | 3.1419235 | -0.0987219 | 6.8580663 | 0.4663090 | 0.0127724 | 0.2445313 | 0.6880867 | 0.0000000 | -8.7928005 | 8.7928005 | 1.8149034 | -6.9663721 | 10.596179 | 26.36222 | 17.22465 | 35.49979 | 2.6767393 | -5.1093776 | 10.4628563 |
| M242 | Cascade Mixed Forest | pacific | 3224 | 3207 | -1.6249863 | 0.0884664 | -2.2081701 | -1.0418026 | 0.9473772 | 0.0057756 | 0.7983673 | 1.0963872 | 6.3242662 | 5.1451565 | 7.5033759 | 5.0717474 | 3.1985456 | 6.944949 | 34.84127 | 31.81115 | 37.87139 | 0.3139076 | 0.2104263 | 0.4173889 |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 1977 | 1807 | -2.3295002 | 0.0674339 | -2.8388339 | -1.8201666 | 0.5558682 | 0.0123817 | 0.3376188 | 0.7741176 | 6.7583368 | 5.4361435 | 8.0805300 | 7.5398852 | 4.7911078 | 10.288663 | 31.57505 | 29.67257 | 33.47754 | 0.1958766 | 0.1186471 | 0.2731062 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 30 | 26 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 367 | 367 | -2.3791059 | 0.1054593 | -3.0177473 | -1.7404646 | 0.2537727 | NA | -0.0561435 | 0.5636888 | 0.0000000 | -10.3328579 | 10.3328579 | 3.2741473 | -7.1573053 | 13.705600 | 58.24883 | 24.55040 | 91.94726 | 2.0199533 | -2.5744450 | 6.6143515 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 1756 | 1756 | -0.6833826 | 0.3885644 | -1.9059857 | 0.5392204 | 0.4898101 | 0.0041572 | 0.3633492 | 0.6162709 | 0.7285113 | 0.3772948 | 1.0797278 | 1.1759506 | 0.7358182 | 1.616083 | 63.66139 | 55.40890 | 71.91389 | 1.1313750 | 0.8149317 | 1.4478184 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 2612 | 2602 | -0.5737481 | 0.3303502 | -1.7008079 | 0.5533117 | 0.6926439 | 0.0035765 | 0.5753726 | 0.8099151 | 0.5800462 | 0.3374351 | 0.8226573 | 1.8715548 | 1.3242808 | 2.418829 | 79.91191 | 70.66262 | 89.16119 | 1.4716127 | 1.2320888 | 1.7111367 |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 1753 | 1742 | -0.1976127 | 0.6453232 | -1.7732331 | 1.3780077 | 0.7504567 | 0.0039630 | 0.6269827 | 0.8739307 | 0.8195240 | 0.4450315 | 1.1940165 | 3.0756917 | 1.9361484 | 4.215235 | 52.55816 | 48.91268 | 56.20364 | 1.2799943 | 1.1206378 | 1.4393509 |
| M334 | Black Hills Coniferous Forest | interior west | 459 | 181 | -3.4114692 | 0.0199885 | -3.6895343 | -3.1334041 | 0.7070400 | 0.0301308 | 0.3656412 | 1.0484388 | 0.0000000 | -91.0939058 | 91.0939058 | 3.8781713 | -86.9058271 | 94.662170 | 73.77076 | 20.35576 | 127.18575 | 2.1007581 | -26.7493369 | 30.9508530 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 220 | 220 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot tau
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning: package 'ggnewscale' was built under R version 4.2.1## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation ideoms with `aes()`## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database
## Warning in grid.Call(C_stringMetric, as.graphicsAnnot(x$label)): font family not
## found in Windows font database## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot alpha (biomass growth compensation effect)
plot a coefficient
## Warning: Removed 15 rows containing missing values (`geom_point()`).
plot b coefficient
## Warning: Removed 15 rows containing missing values (`geom_point()`).
plot c coefficient
## Warning: Removed 1 rows containing missing values (`geom_hline()`).## Warning: Removed 15 rows containing missing values (`geom_point()`).
plot d coefficient
## Warning: Removed 15 rows containing missing values (`geom_point()`).
Caclulations - weighted averages
tau (stand biomass productivity in % 2000-2021)
## region weighted.tau weighted.tau.std_Error 95 % CI, upper
## 1 entire US 0.79308110 0.08157866 0.9529752745
## 2 pacific -0.16263683 0.01835199 -0.1266669228
## 3 east 1.03388386 0.06860422 1.1683481326
## 4 interior west -0.07816593 0.04014652 0.0005212438
## 95 % CI, lower
## 1 0.6331869
## 2 -0.1986067
## 3 0.8994196
## 4 -0.1568531alpha (biomass growth compensation effect)
## region weighted.alpha weighted.alpha.std_Error 95 % CI, upper
## 1 entire US 0.45776092 0.011261773 0.47983399
## 2 pacific 0.06979184 0.005454635 0.08048293
## 3 east 0.31562087 0.009064148 0.33338659
## 4 interior west 0.07234821 0.003862086 0.07991790
## 95 % CI, lower
## 1 0.43568785
## 2 0.05910076
## 3 0.29785514
## 4 0.06477852Analaysis 2: \(G_{TI-NoIngrow}\)
211 - Northeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6869 3216.6
## 2 6817 3001.3 52 215.24 9.4015 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 24882.32
## 2 2 24354.11##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.16043 0.28343 4.094 4.29e-05 ***
## alpha 0.83630 0.03869 21.616 < 2e-16 ***
## a 0.50001 0.04546 10.998 < 2e-16 ***
## b 1.83267 0.09803 18.695 < 2e-16 ***
## c 68.73905 1.24051 55.412 < 2e-16 ***
## d 0.97466 0.03780 25.782 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6635 on 6817 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (54 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 1 rows containing missing values (`geom_point()`).
plotting 2
212 - Laurentian Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 19351 23127
## 2 18862 21107 489 2019.9 3.6913 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 63998.00
## 2 2 61697.03##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.66279 0.25717 6.466 1.03e-10 ***
## alpha 1.04253 0.02698 38.646 < 2e-16 ***
## a 0.27895 0.01349 20.673 < 2e-16 ***
## b 1.24888 0.04750 26.290 < 2e-16 ***
## c 60.91140 0.79454 76.663 < 2e-16 ***
## d 1.01774 0.01990 51.144 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.058 on 18862 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (3847 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 40 rows containing missing values (`geom_point()`).
plotting 2
221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 7319 3643.2
## 2 7255 3434.1 64 209.17 6.9047 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 31554.54
## 2 2 30990.74##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.65757 0.14783 -4.448 8.79e-06 ***
## alpha 0.84728 0.04206 20.145 < 2e-16 ***
## a 0.94746 0.18176 5.213 1.91e-07 ***
## b 3.02566 0.20483 14.772 < 2e-16 ***
## c 62.64769 2.62270 23.887 < 2e-16 ***
## d 1.49835 0.11404 13.139 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.688 on 7255 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (72 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 4 rows containing missing values (`geom_point()`).
plotting 2
222 - Midwest Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 5044 4308.4
## 2 4824 3738.3 220 570.14 3.3442 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 19723.24
## 2 2 18609.23##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.30609 0.28763 1.064 0.287
## alpha 0.99466 0.04942 20.127 <2e-16 ***
## a 0.58787 0.05500 10.689 <2e-16 ***
## b 2.24090 0.12610 17.772 <2e-16 ***
## c 63.29329 1.67388 37.812 <2e-16 ***
## d 1.06029 0.04909 21.601 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8803 on 4824 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1015 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 8 rows containing missing values (`geom_point()`).
plotting 2
223 - Central Interior Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8872 6007.8
## 2 8730 5691.6 142 316.19 3.4153 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 34828.86
## 2 2 34001.35##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.80380 0.12364 -6.501 8.42e-11 ***
## alpha 0.75737 0.04413 17.163 < 2e-16 ***
## a 0.85355 0.17271 4.942 7.87e-07 ***
## b 2.44175 0.18175 13.435 < 2e-16 ***
## c 50.68176 1.21271 41.792 < 2e-16 ***
## d 1.30262 0.09013 14.452 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8074 on 8730 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (1274 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 5 rows containing missing values (`geom_point()`).
plotting 2
231 - Southeastern Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 13446 12254
## 2 13195 11035 251 1218.4 5.8042 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 66367.52
## 2 2 64659.57##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.76047 0.25552 6.89 5.84e-12 ***
## alpha 0.94544 0.02543 37.18 < 2e-16 ***
## a 0.46747 0.03053 15.31 < 2e-16 ***
## b 3.08669 0.12281 25.13 < 2e-16 ***
## c 34.31153 0.51813 66.22 < 2e-16 ***
## d 1.27386 0.02659 47.91 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9145 on 13195 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (316 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 27 rows containing missing values (`geom_point()`).
plotting 2
232 - Outer Coastal Plain Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 13504 15331
## 2 13221 13799 283 1531.5 5.185 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 65934.14
## 2 2 64083.77##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.38805 0.25226 5.502 3.81e-08 ***
## alpha 0.96451 0.02494 38.666 < 2e-16 ***
## a 0.39168 0.03382 11.580 < 2e-16 ***
## b 2.90579 0.12250 23.720 < 2e-16 ***
## c 32.75286 0.52980 61.821 < 2e-16 ***
## d 1.31061 0.02994 43.781 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.022 on 13221 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (402 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 57 rows containing missing values (`geom_point()`).
plotting 2
234 - Lower Mississippi Riverine Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1368 2135.0
## 2 1316 1984.8 52 150.18 1.9149 0.0001261 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 7497.623
## 2 2 7297.936##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.5051 0.9766 0.517 0.605
## alpha 0.9019 0.1202 7.500 1.17e-13 ***
## a 0.8976 0.2284 3.930 8.95e-05 ***
## b 2.8449 0.5751 4.947 8.51e-07 ***
## c 41.4473 3.4014 12.185 < 2e-16 ***
## d 1.1039 0.1655 6.670 3.76e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.228 on 1316 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (66 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.84765, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.5987, p-value = 4.252e-06
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 2 rows containing missing values (`geom_point()`).
plotting 2
242 - Pacific Lowland Mixed Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
251 - Prairie Parkland (Temperate)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1888 1534.7
## 2 1773 1348.7 115 185.99 2.1261 2.171e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 6937.884
## 2 2 6459.937##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.0492 0.5646 1.858 0.0633 .
## alpha 0.4953 0.1033 4.794 1.77e-06 ***
## a 0.7461 0.1645 4.537 6.10e-06 ***
## b 1.3449 0.2007 6.700 2.79e-11 ***
## c 55.2406 2.6391 20.931 < 2e-16 ***
## d 1.1183 0.1392 8.033 1.72e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8722 on 1773 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (516 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90528, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -10.817, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 9 rows containing missing values (`geom_point()`).
plotting 2
255 - Prairie Parkland (Subtropical)
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 710 3128.3
## 2 667 3030.4 43 97.922 0.5012 0.997
## model AIC
## 1 1 3570.774
## 2 2 3515.063##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 5.00000 7.01224 0.713 0.4761
## alpha 0.61584 0.29378 2.096 0.0364 *
## a 0.12245 0.09309 1.315 0.1888
## b 1.09126 0.80152 1.361 0.1738
## c 29.62861 2.55155 11.612 < 2e-16 ***
## d 0.88881 0.14392 6.176 1.14e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.132 on 667 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (44 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.89301, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.9434, p-value = 7.678e-07
## alternative hypothesis: two.sidedpredict and plot
plotting 2
261 - California Coastal Chaparral Forest and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
262 - California Dry Steppe
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
263 - California Coastal Steppe - Mixed Forest and Redwood Forest
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
313 - Colorado Plateau Semi-Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
315 - Southwest Plateau and Plains Dry Steppe and Shrub
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
321 - Chihuahuan Semi-Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
322 - American Semidesert and Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
331 - Great Plains/Palouse Dry Steppe
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
332 - Great Plains Steppe
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
341 - Intermountain Semi-desert & Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
342 - Intermountain Semi-Desert
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
411 - Everglades
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M211 - Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 6765 3120.3
## 2 6741 2911.9 24 208.47 20.108 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 23241.99
## 2 2 22770.74##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.56276 0.34187 4.571 4.94e-06 ***
## alpha 0.82601 0.03582 23.058 < 2e-16 ***
## a 0.26185 0.03568 7.339 2.40e-13 ***
## b 1.76955 0.10462 16.914 < 2e-16 ***
## c 74.40239 1.70488 43.641 < 2e-16 ***
## d 1.08187 0.03872 27.938 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6572 on 6741 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (25 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
plotting 2
M221 - Eastern Broadleaf Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 8308 4928.4
## 2 8253 4767.3 55 161.05 5.069 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 37694.53
## 2 2 37285.40##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 1.17350 0.26044 4.506 6.70e-06 ***
## alpha 0.90599 0.05733 15.804 < 2e-16 ***
## a 0.67282 0.16239 4.143 3.46e-05 ***
## b 2.34356 0.18881 12.412 < 2e-16 ***
## c 47.55653 1.19002 39.963 < 2e-16 ***
## d 1.37435 0.09108 15.090 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.76 on 8253 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (56 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
predict and plot
## Warning: Removed 2 rows containing missing values (`geom_point()`).
plotting 2
M223 - Ozark Broadleaf Forest Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 890 702.27
## 2 883 680.17 7 22.094 4.0975 0.0001935 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 3537.006
## 2 2 3503.262##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 4.2321 2.3616 1.792 0.07347 .
## alpha 1.0090 0.1732 5.827 7.92e-09 ***
## a 0.0000 0.2513 0.000 1.00000
## b 1.3190 0.4329 3.047 0.00238 **
## c 42.4816 2.7686 15.344 < 2e-16 ***
## d 1.2773 0.2536 5.036 5.77e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8777 on 883 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (7 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96657, p-value = 2.1e-13
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.1895, p-value = 0.001425
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M231 - Ouachita Mixed Forest
model selection 1
## model AIC
## 1 1 4182.168
## 2 2 4126.965##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 5.0000 3.2307 1.548 0.1220
## alpha 0.8349 0.1325 6.302 4.43e-10 ***
## a 0.0000 0.2800 0.000 1.0000
## b 1.1840 0.4826 2.453 0.0143 *
## c 45.8648 5.6255 8.153 1.07e-15 ***
## d 1.7341 0.4164 4.165 3.39e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9318 on 987 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (13 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93432, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.534, p-value = 6.404e-11
## alternative hypothesis: two.sidedpredict and plot
plotting 2
M242 - Cascade Mixed Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 3140 2906.2
## 2 3127 2742.5 13 163.73 14.36 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 16154.59
## 2 2 15943.37##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.62004 0.29143 -5.559 2.94e-08 ***
## alpha 1.07370 0.07235 14.840 < 2e-16 ***
## a 0.00000 2.04174 0.000 1.00000
## b 6.03100 2.07752 2.903 0.00372 **
## c 95.71565 6.88773 13.897 < 2e-16 ***
## d 2.47175 0.59045 4.186 2.91e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9365 on 3127 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (91 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.90935, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -13.763, p-value < 2.2e-16
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 17 rows containing missing values (`geom_point()`).
plotting 2
M261 - Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1681 1785.5
## 2 1668 1729.8 13 55.706 4.1321 8.879e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 7935.257
## 2 2 7839.811##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -2.2749 0.2530 -8.993 < 2e-16 ***
## alpha 0.8288 0.1093 7.585 5.50e-14 ***
## a 3.6697 0.8554 4.290 1.89e-05 ***
## b 3.3994 0.8311 4.090 4.51e-05 ***
## c 41.8919 8.2424 5.082 4.14e-07 ***
## d 1.3979 0.4362 3.205 0.00138 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.018 on 1668 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (303 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.9102, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.1525, p-value = 0.001619
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 10 rows containing missing values (`geom_point()`).
plotting 2
M262 - Califormia Coastal Range = Coniferous Forest - Open woodland Shrub Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
M313 - Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 360 195.96
## 2 359 188.52 1 7.4343 14.157 0.0001964 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 988.1197
## 2 2 976.0028##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -2.6700 0.2688 -9.932 < 2e-16 ***
## alpha 0.6216 0.1517 4.097 5.18e-05 ***
## a 0.0000 2.0273 0.000 1.0000
## b 3.3517 2.0895 1.604 0.1096
## c 70.7341 13.5838 5.207 3.24e-07 ***
## d 1.6872 0.9213 1.831 0.0679 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7247 on 359 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (2 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.93536, p-value = 1.71e-11
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -1.1936, p-value = 0.2326
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 1 rows containing missing values (`geom_point()`).
plotting 2
M331 - Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1736 1676.4
## 2 1719 1561.1 17 115.28 7.467 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 4364.968
## 2 2 4242.489##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.33750 1.03019 0.328 0.743
## alpha 0.74430 0.06142 12.117 < 2e-16 ***
## a 0.47885 0.11135 4.300 1.80e-05 ***
## b 0.71388 0.15703 4.546 5.85e-06 ***
## c 81.01354 4.97015 16.300 < 2e-16 ***
## d 0.89656 0.10601 8.458 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.953 on 1719 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (31 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.87825, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -5.6786, p-value = 1.358e-08
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 11 rows containing missing values (`geom_point()`).
plotting 2
M332 - Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 2527 2344.7
## 2 2485 2116.1 42 228.58 6.3913 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 8552.063
## 2 2 8324.449##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -0.65832 0.55077 -1.195 0.232
## alpha 0.95413 0.05599 17.041 < 2e-16 ***
## a 0.45198 0.07716 5.858 5.32e-09 ***
## b 1.90207 0.26351 7.218 6.97e-13 ***
## c 89.95195 4.04062 22.262 < 2e-16 ***
## d 1.25137 0.07441 16.817 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9228 on 2485 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (121 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.86245, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -4.7053, p-value = 2.535e-06
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 22 rows containing missing values (`geom_point()`).
plotting 2
M333 - Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 1699 1220.6
## 2 1670 1059.2 29 161.46 8.7783 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 6534.582
## 2 2 6295.314##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau 0.90446 1.28317 0.705 0.481
## alpha 0.97795 0.06105 16.019 < 2e-16 ***
## a 0.45844 0.11249 4.075 4.81e-05 ***
## b 2.33387 0.54745 4.263 2.13e-05 ***
## c 63.93883 1.88255 33.964 < 2e-16 ***
## d 1.08203 0.04659 23.225 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7964 on 1670 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (77 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.91344, p-value < 2.2e-16
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -6.206, p-value = 5.434e-10
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 15 rows containing missing values (`geom_point()`).
plotting 2
M334 - Black Hills Coniferous Forest
model selection 1
## Analysis of Variance Table
##
## Model 1: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Model 2: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) * tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
## Res.Df Res.Sum Sq Df Sum Sq F value Pr(>F)
## 1 357 618.39
## 2 349 584.57 8 33.819 2.5238 0.01114 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## model AIC
## 1 1 1072.454
## 2 2 1042.319##
## Formula: G_obs_TreeInc_NoIngrow_MgHaYr ~ (1 + (MEASTIME_avg - 1990) *
## tau/100) * (1 - alpha * B_L_prop) * (a + b * exp(-((log(STDAGE_t1/c))/d)^2))
##
## Parameters:
## Estimate Std. Error t value Pr(>|t|)
## tau -1.1952 0.9020 -1.325 0.186
## alpha 0.6866 0.1670 4.110 4.93e-05 ***
## a 0.0000 7.8286 0.000 1.000
## b 1.4054 7.8112 0.180 0.857
## c 125.1995 121.6202 1.029 0.304
## d 2.5030 9.5252 0.263 0.793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.294 on 349 degrees of freedom
##
## Algorithm "port", convergence message: relative convergence (4)
## (104 observations deleted due to missingness)summary
- simple model: fits
- alpha model: fits
plot residuals
##
## ------
## Shapiro-Wilk normality test
##
## data: stdres
## W = 0.96219, p-value = 6.081e-08
##
##
## ------##
## Runs Test
##
## data: as.factor(run)
## Standard Normal = -3.952, p-value = 7.749e-05
## alternative hypothesis: two.sidedpredict and plot
## Warning: Removed 4 rows containing missing values (`geom_point()`).
plotting 2
M341 - Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow
model selection 1
summary
- simple model: does not fit
- alpha model: does not fit
plot residuals
## [1] "cannot plot residuals"predict and plot
## [1] "cannot plot data with prediction"plotting 2
Fitted parameters
Best / selected models by ecoprovince
| Code | Ecoregion | Sel.Mod |
|---|---|---|
| 211 | Northeastern Mixed Forest | 2 |
| 212 | Laurentian Mixed Forest | 2 |
| 221 | Eastern Broadleaf Forest | 2 |
| 222 | Midwest Broadleaf Forest | 2 |
| 223 | Central Interior Broadleaf Forest | 2 |
| 231 | Southeastern Mixed Forest | 2 |
| 232 | Outer Coastal Plain Mixed Forest | 2 |
| 234 | Lower Mississippi Riverine Forest | 2 |
| 242 | Pacific Lowland Mixed Forest | NA |
| 251 | Prairie Parkland (Temperate) | 2 |
| 255 | Prairie Parkland (Subtropical) | 2 |
| 261 | California Coastal Chaparral Forest and Shrub | NA |
| 262 | California Dry Steppe | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | NA |
| 313 | Colorado Plateau Semi-Desert | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | NA |
| 321 | Chihuahuan Semi-Desert | NA |
| 322 | American Semidesert and Desert | NA |
| 331 | Great Plains/Palouse Dry Steppe | NA |
| 332 | Great Plains Steppe | NA |
| 341 | Intermountain Semi-Desert and Desert | NA |
| 342 | Intermountain Semi-Desert | NA |
| 411 | Everglades | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | 2 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | 2 |
| M223 | Ozark Broadleaf Forest Meadow | 2 |
| M231 | Ouachita Mixed Forest | 2 |
| M242 | Cascade Mixed Forest | 2 |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | 2 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | 2 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | 2 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | 2 |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | 2 |
| M334 | Black Hills Coniferous Forest | 2 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | NA |
table by ecoprovince
| Code | Ecoregion | region | n.obs | n.plots | tau | tau.variance | tau.2.5 | tau.97.5 | alpha | alpha.variance | alpha.2.5 | alpha.97.5 | a | a.2.5 | a.97.5 | b | b.2.5 | b.97.5 | c | c.2.5 | c.97.5 | d | d.2.5 | d.97.5 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 211 | Northeastern Mixed Forest | east | 6877 | 2876 | 1.1604259 | 0.0803348 | 0.6048069 | 1.7160449 | 0.8362965 | 0.0014969 | 0.7604536 | 0.9121393 | 0.5000053 | 0.4108841 | 0.5891266 | 1.8326695 | 1.6405045 | 2.024834 | 68.73905 | 66.30725 | 71.17085 | 0.9746629 | 0.9005549 | 1.048771 |
| 212 | Laurentian Mixed Forest | east | 22715 | 9499 | 1.6627923 | 0.0661342 | 1.1587245 | 2.1668601 | 1.0425348 | 0.0007277 | 0.9896582 | 1.0954114 | 0.2789529 | 0.2525040 | 0.3054017 | 1.2488840 | 1.1557702 | 1.341998 | 60.91140 | 59.35403 | 62.46877 | 1.0177355 | 0.9787307 | 1.056740 |
| 221 | Eastern Broadleaf Forest | east | 7333 | 3571 | -0.6575707 | 0.0218534 | -0.9473585 | -0.3677829 | 0.8472846 | 0.0017689 | 0.7648373 | 0.9297318 | 0.9474617 | 0.5911585 | 1.3037649 | 3.0256591 | 2.6241391 | 3.427179 | 62.64769 | 57.50643 | 67.78896 | 1.4983497 | 1.2748002 | 1.721899 |
| 222 | Midwest Broadleaf Forest | east | 5845 | 2589 | 0.3060902 | 0.0827332 | -0.2578033 | 0.8699837 | 0.9946592 | 0.0024422 | 0.8977762 | 1.0915421 | 0.5878730 | 0.4800546 | 0.6956914 | 2.2408996 | 1.9936958 | 2.488103 | 63.29329 | 60.01172 | 66.57487 | 1.0602947 | 0.9640641 | 1.156525 |
| 223 | Central Interior Broadleaf Forest | east | 10010 | 3864 | -0.8037979 | 0.0152878 | -1.0461690 | -0.5614269 | 0.7573657 | 0.0019472 | 0.6708652 | 0.8438662 | 0.8535535 | 0.5150103 | 1.1920968 | 2.4417477 | 2.0854721 | 2.798023 | 50.68176 | 48.30456 | 53.05895 | 1.3026216 | 1.1259365 | 1.479307 |
| 231 | Southeastern Mixed Forest | east | 13517 | 6193 | 1.7604710 | 0.0652897 | 1.2596180 | 2.2613239 | 0.9454435 | 0.0006466 | 0.8956020 | 0.9952850 | 0.4674666 | 0.4076299 | 0.5273034 | 3.0866903 | 2.8459632 | 3.327418 | 34.31153 | 33.29592 | 35.32713 | 1.2738604 | 1.2217378 | 1.325983 |
| 232 | Outer Coastal Plain Mixed Forest | east | 13629 | 6626 | 1.3880549 | 0.0636354 | 0.8935880 | 1.8825218 | 0.9645061 | 0.0006222 | 0.9156109 | 1.0134013 | 0.3916840 | 0.3253825 | 0.4579854 | 2.9057852 | 2.6656656 | 3.145905 | 32.75286 | 31.71437 | 33.79135 | 1.3106095 | 1.2519320 | 1.369287 |
| 234 | Lower Mississippi Riverine Forest | east | 1388 | 778 | 0.5050705 | 0.9537550 | -1.4107998 | 2.4209409 | 0.9019106 | 0.0144595 | 0.6660129 | 1.1378083 | 0.8975931 | 0.4494886 | 1.3456977 | 2.8448715 | 1.7167162 | 3.973027 | 41.44734 | 34.77454 | 48.12015 | 1.1039359 | 0.7792314 | 1.428640 |
| 242 | Pacific Lowland Mixed Forest | pacific | 83 | 83 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 251 | Prairie Parkland (Temperate) | east | 2295 | 906 | 1.0492351 | 0.3187643 | -0.0581012 | 2.1565713 | 0.4953403 | 0.0106756 | 0.2926932 | 0.6979874 | 0.7461268 | 0.4235561 | 1.0686975 | 1.3449033 | 0.9512167 | 1.738590 | 55.24059 | 50.06447 | 60.41671 | 1.1183398 | 0.8452875 | 1.391392 |
| 255 | Prairie Parkland (Subtropical) | east | 717 | 319 | 5.0000000 | 49.1714701 | -8.7687168 | 18.7687168 | 0.6158412 | 0.0863059 | 0.0389990 | 1.1926834 | 0.1224478 | -0.0603301 | 0.3052256 | 1.0912636 | -0.4825347 | 2.665062 | 29.62861 | 24.61857 | 34.63866 | 0.8888098 | 0.6062183 | 1.171401 |
| 261 | California Coastal Chaparral Forest and Shrub | pacific | 25 | 25 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 262 | California Dry Steppe | pacific | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 263 | California Coastal Steppe - Mixed Forest and Redwood Forest | pacific | 163 | 161 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 313 | Colorado Plateau Semi-Desert | interior west | 218 | 218 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 315 | Southwest Plateau and Plains Dry Steppe and Shrub | interior west | 4 | 4 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 321 | Chihuahuan Semi-Desert | interior west | 9 | 9 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 322 | American Semidesert and Desert | interior west | 3 | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 331 | Great Plains/Palouse Dry Steppe | interior west | 331 | 255 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 332 | Great Plains Steppe | interior west | 232 | 128 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 341 | Intermountain Semi-Desert and Desert | interior west | 66 | 64 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 342 | Intermountain Semi-Desert | interior west | 124 | 123 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 411 | Everglades | east | 96 | 63 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M211 | Adirondack-New England Mixed forest - Coniferous Forest - Alpine Meadow | east | 6772 | 3006 | 1.5627630 | 0.1168750 | 0.8925900 | 2.2329360 | 0.8260140 | 0.0012833 | 0.7557899 | 0.8962380 | 0.2618459 | 0.1919077 | 0.3317841 | 1.7695542 | 1.5644627 | 1.974646 | 74.40239 | 71.06028 | 77.74450 | 1.0818700 | 1.0059582 | 1.157782 |
| M221 | Central Appalachian Broadleaf Forest - Coniferous Forest - Meadow | east | 8315 | 3810 | 1.1734964 | 0.0678271 | 0.6629757 | 1.6840171 | 0.9059890 | 0.0032864 | 0.7936131 | 1.0183650 | 0.6728182 | 0.3544915 | 0.9911450 | 2.3435560 | 1.9734481 | 2.713664 | 47.55653 | 45.22380 | 49.88927 | 1.3743461 | 1.1958080 | 1.552884 |
| M223 | Ozark Broadleaf Forest Meadow | east | 896 | 349 | 4.2321282 | 5.5772489 | -0.4029152 | 8.8671716 | 1.0089725 | 0.0299867 | 0.6691064 | 1.3488385 | 0.0000000 | -0.4931357 | 0.4931357 | 1.3190460 | 0.4694354 | 2.168657 | 42.48165 | 37.04789 | 47.91541 | 1.2773301 | 0.7795053 | 1.775155 |
| M231 | Ouachita Mixed Forest | east | 1006 | 495 | 5.0000000 | 10.4375433 | -1.3398667 | 11.3398667 | 0.8348546 | 0.0175510 | 0.5748792 | 1.0948300 | 0.0000000 | -0.5495270 | 0.5495270 | 1.1840165 | 0.2369666 | 2.131066 | 45.86482 | 34.82547 | 56.90416 | 1.7340675 | 0.9169601 | 2.551175 |
| M242 | Cascade Mixed Forest | pacific | 3224 | 3207 | -1.6200395 | 0.0849295 | -2.1914466 | -1.0486325 | 1.0737045 | 0.0052348 | 0.9318430 | 1.2155661 | 0.0000000 | -4.0032924 | 4.0032924 | 6.0310044 | 1.9575578 | 10.104451 | 95.71565 | 82.21073 | 109.22058 | 2.4717520 | 1.3140355 | 3.629469 |
| M261 | Sierran Steppe - Mixed Forest - Coniferous Forest - Alpine Meadow | pacific | 1977 | 1807 | -2.2748631 | 0.0639943 | -2.7710372 | -1.7786890 | 0.8287531 | 0.0119390 | 0.6144409 | 1.0430652 | 3.6696912 | 1.9919430 | 5.3474395 | 3.3993748 | 1.7693322 | 5.029417 | 41.89193 | 25.72538 | 58.05847 | 1.3978848 | 0.5422963 | 2.253473 |
| M262 | California Coastal Range Coniferous Forest - Open Woodland - Shrub - Meadow | interior west | 30 | 26 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| M313 | Arizona-New Mexico Mountains Semi-Desert - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 367 | 367 | -2.6699792 | 0.0722686 | -3.1986552 | -2.1413033 | 0.6216039 | 0.0230193 | 0.3232302 | 0.9199776 | 0.0000000 | -3.9868125 | 3.9868125 | 3.3516574 | -0.7574843 | 7.460799 | 70.73407 | 44.02026 | 97.44787 | 1.6872174 | -0.1245214 | 3.498956 |
| M331 | Southern Rocky Mountain Steppe - Open Woodland - Coniferous Forest - Alpine Meadow | interior west | 1756 | 1756 | 0.3375011 | 1.0612959 | -1.6830611 | 2.3580633 | 0.7443000 | 0.0037730 | 0.6238247 | 0.8647754 | 0.4788522 | 0.2604549 | 0.6972496 | 0.7138755 | 0.4058792 | 1.021872 | 81.01354 | 71.26537 | 90.76171 | 0.8965640 | 0.6886500 | 1.104478 |
| M332 | Middle Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 2612 | 2602 | -0.6583167 | 0.3033530 | -1.7383417 | 0.4217083 | 0.9541320 | 0.0031350 | 0.8443378 | 1.0639262 | 0.4519820 | 0.3006746 | 0.6032894 | 1.9020737 | 1.3853496 | 2.418798 | 89.95195 | 82.02862 | 97.87527 | 1.2513715 | 1.1054579 | 1.397285 |
| M333 | Northern Rocky Mountain Steppe - Coniferous Forest - Alpine Meadow | interior west | 1753 | 1742 | 0.9044642 | 1.6465338 | -1.6123334 | 3.4212618 | 0.9779452 | 0.0037271 | 0.8582027 | 1.0976877 | 0.4584394 | 0.2378010 | 0.6790778 | 2.3338740 | 1.2601154 | 3.407633 | 63.93883 | 60.24642 | 67.63123 | 1.0820320 | 0.9906527 | 1.173411 |
| M334 | Black Hills Coniferous Forest | interior west | 459 | 181 | -1.1951917 | 0.8135475 | -2.9691698 | 0.5787863 | 0.6866001 | 0.0279025 | 0.3580674 | 1.0151329 | 0.0000000 | -15.3972638 | 15.3972638 | 1.4053772 | -13.9576020 | 16.768356 | 125.19953 | -114.00112 | 364.40018 | 2.5029949 | -16.2309912 | 21.236981 |
| M341 | Nevada-Utah Mountains Semi-Desert - Coniferous Forest - Alpine Meadow | interior west | 220 | 220 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
plot tau
map
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\hogan.jaaron\Dropbox\FIA_R\Mapping\S_USA.EcoMapProvinces\S_USA.EcoMapProvinces.shp", layer: "S_USA.EcoMapProvinces"
## with 37 features
## It has 17 fields
## Integer64 fields read as strings: PROVINCE_ PROVINCE_I## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
## family not found in Windows font database
plot alpha (biomass growth compensation effect)
plot a coefficient
## Warning: Removed 15 rows containing missing values (`geom_point()`).
plot b coefficient
## Warning: Removed 15 rows containing missing values (`geom_point()`).
plot c coefficient
## Warning: Removed 1 rows containing missing values (`geom_hline()`).## Warning: Removed 19 rows containing missing values (`geom_point()`).
plot d coefficient
## Warning: Removed 15 rows containing missing values (`geom_point()`).
Caclulations - weighted averages
tau (stand biomass growth enhancement factor in % 2000-2021)
## region weighted.tau weighted.tau.std_Error 95 % CI, upper
## 1 entire US 0.70060747 0.10293723 0.90236445
## 2 pacific -0.16065851 0.01796126 -0.12545444
## 3 east 0.87405707 0.08477051 1.04020727
## 4 interior west -0.01279109 0.05556463 0.09611559
## 95 % CI, lower
## 1 0.4988505
## 2 -0.1958626
## 3 0.7079069
## 4 -0.1216978alpha (biomass growth compensation effect)
## region weighted.alpha weighted.alpha.std_Error 95 % CI, upper
## 1 entire US 0.8962475 0.011356331 0.9185059
## 2 pacific 0.0852987 0.005259694 0.0956077
## 3 east 0.7100316 0.009324231 0.7283071
## 4 interior west 0.1009172 0.003789538 0.1083446
## 95 % CI, lower
## 1 0.87398906
## 2 0.07498970
## 3 0.69175612
## 4 0.09348966